Search Results (4,014)

Background: Migraine diagnosis and severity staging rest on clinical interviews according to the ICHD-3 criteria, with no validated objective biomarker. Functional near-infrared spectroscopy (fNIRS) is a portable and non-invasive method, but existing fNIRS migraine classification studies remain relatively small and rely on cognitive tasks. We assessed whether prefrontal hemodynamic responses with a head-down-to-knees maneuver separate migraine patients from controls, and high- from low-severity migraine. Methods: Prefrontal fNIRS, including short separation channels, was recorded during the maneuver in 50 interictal migraine patients and 52 controls screened for the absence of migraine, serious chronic conditions and hypertension. Nine hemodynamic parameters per chromophore (HbO, Hb and HbT) across thirty channels entered a hypothesis-neutral pipeline of 270 candidate pipelines (3 chromophores × 3 feature selection strategies × 30 classifiers) with no predefined region of interest. Results: Deoxyhemoglobin features selected by embedded L1 regularization with shrinkage-regularized linear discriminant analysis separated the groups with 92% balanced accuracy on the internal hold-out (92% sensitivity, 92% specificity, ROC-AUC 0.99; permutation p = 3 × 10⁻⁴), against 87% in development-set cross-validation and 88% under nested selection cross-validation, with a selection bias of +0.03. High- vs. low-severity classification (19 high, 31 low) did not exceed chance under nested validation (45%; permutation p = 0.45). Conclusions: A wide-scale pipeline achieved a robust, validated separation of migraine from screened controls, carried by a distributed venous-weighted deoxyhemoglobin signature; specificity against other headache disorders remains to be established. Attack frequency severity was not separable above chance, indicating that scalar hemodynamic descriptors are sufficient for a categorical but not a graded contrast. Full article

Wearable brain-imaging devices have been developed to meet the growing demand in the healthcare industry for long-term monitoring of brain signals in natural conditions, such as monitoring brain diseases and emotions. However, conventional EEG and fNIRS (functional near-infrared spectroscopy) devices are often expensive, bulky and difficult to operate, making it difficult to monitor patients for long periods in natural conditions. To address these issues, this article proposes a low-cost, portable and multimodal wearable brain signal acquisition scheme. It combines EEG (electroencephalography) and fNIRS to reflect brain activity from different perspectives. In order to make it more wearable, a conductive rubber material is used as the electrode for the EEG. In this study, the corresponding experiments were used to verify the performance of the device. The first is the measurement of internal system noise, which satisfies the data acquisition of EEG and fNIRS at different gain levels. The α-rhythm experiment and the SSVEP (steady-state visual evoked potentials) experiment were used to validate the performance of EEG data acquisition. The performance of the fNIRS was verified by measuring changes in cerebral blood oxygen during breath-hold and breathing. In addition, by decomposing the raw fNIRS data with the VMD (variational mode decomposition) algorithm and performing correlation analysis, heart rate information was separated from the data. The performance of the proposed device was validated in the above experiments, confirming the feasibility of the design for multimodal data acquisition and meeting the requirements for portability and wearability. Furthermore, the proposed device was tested with 31 subjects (15 depressive subjects) to detect depression. Experiments proved the effectiveness of the multimodal signals, which outperformed single modal and surpassed EEG by 8.4% and fNIRS by 23.5%. Full article

Transparent glazing systems are increasingly required to provide simultaneous control over light transmission, solar heat gain, thermal losses, surface contamination, and environmental durability, creating new challenges for the development of multifunctional coating technologies. This review critically examines advanced optical and self-cleaning coatings developed for transparent glass and polymeric substrates, with particular emphasis on the relationships between materials chemistry, surface/interface chemistry, microstructure, and functional performance. Dielectric multilayers, metal oxides, ceramic coatings, sol-gel-derived hybrid systems, and emerging chromogenic materials are discussed in terms of their chemical compositions, structural characteristics, and mechanisms governing optical, thermal, and surface properties. Particular attention is given to structure–property relationships associated with photosynthetically active radiation (PAR) transmission, near-infrared (NIR) management, thermal emissivity, solar modulation, wettability, and self-cleaning behavior, together with their implications for energy-efficient transparent glazing and greenhouse environments. The influence of coating architecture, porosity, surface roughness, interfacial interactions, and deposition conditions on functional performance and long-term stability is critically evaluated. The advantages and limitations of representative deposition strategies are further compared, considering scalability, process compatibility, substrate sensitivity, and application to heat-sensitive polymeric films. Environmental degradation mechanisms induced by ultraviolet irradiation, moisture, thermal cycling, and mechanical stresses are analyzed to identify the key factors governing coating durability and sustainability. Finally, current knowledge gaps and emerging research directions are identified, highlighting the need for rational materials design, multifunctional integration, scalable fabrication, and improved structure-property-durability correlations for next-generation transparent glazing and greenhouse applications. Full article

Rapid and non-destructive assessment of sweet potato quality is important for postharvest management, processing suitability evaluation, and market quality control. In this study, a 12-channel visible/near-infrared (Vis/NIR) spectral sensing system was applied to predict multiple physicochemical quality attributes of postharvest sweet potatoes. Sixty independent sweet potato storage roots were measured at three representative positions, producing 180 position-specific observations; measurements from the same root were retained within the same validation group. The measured attributes included dry matter content (DMC), starch content (SC), soluble solids content (SSC), and the CIE 1976 L*a*b* (CIELAB) color coordinates L*, a*, and b*. Four spectral treatment conditions, including original spectra, normalization, standardization, and first-derivative transformation, were combined with partial least squares regression (PLSR), multiple linear regression (MLR), extreme gradient boosting (XGBoost), and random forest (RF), generating 16 prediction strategies for each quality attribute. Root-grouped five-fold cross-validation showed that the optimal models achieved coefficients of determination for cross-validation (R²CV) ranging from 0.9083 to 0.9190 and residual predictive deviation (RPD) values ranging from 3.3112 to 3.5230. Repeated grouped cross-validation produced mean R²CV values of 0.9113–0.9176, and root-block Y-scrambling yielded empirical p values of 0.005 for all six attributes. PLSR provided the highest cross-validated performance for all six quality attributes, although MLR showed comparable performance for several targets. These results provide preliminary evidence that discrete Vis/NIR spectral sensing can support simultaneous non-destructive estimation of multiple sweet potato quality attributes. External multi-batch and multi-cultivar validation is required before the models can be considered robust for practical deployment. Full article

This study evaluates operational optical to near-infrared (NIR) band (400–1050 nm) atmospheric transmittance simulation at the Ali-CPT site (5250 m above sea level). Monthly mean profiles from a 20-year Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were combined with precipitable water vapor (PWV) constraints from the POM-02 sun photometer and validated against DTF-8 sun photometer measurements under fixed aerosol parameters (visibility of 75 km, Clean Continental mode). Mode 1 used real-time lidar extinction profiles; Mode 2 used built-in aerosol modes scaled by aerosol optical depth (AOD). Both modes achieved correlation coefficients greater than 0.93. Mode 1 showed root mean square errors (RMSEs) of 0.034–0.040 in the 400–870 nm range, while Mode 2 exhibited a systematic negative bias (RMSE 0.040–0.052) due to the mismatch between the fixed visibility assumption and the much cleaner winter conditions. The errors in the 940 nm water vapor absorption band were dominated by the vertical structural deviation of the MERRA-2 water vapor profile during high-PWV summer conditions. The results confirm the feasibility of reanalysis-based operational transmittance simulation at data-sparse high-altitude sites. Full article

Surface wettability is a critical factor influencing the biological performance of orthopedic Ti implants. The native TiO₂ film on Ti undergoes changes in wettability under ultraviolet (UV) irradiation. However, the limited tissue penetration of UV light compared with near-infrared (NIR) light restricts its potential clinical application. In this study, a black TiO₂ (b-TiO₂) coating with NIR-responsive wettability was fabricated directly on a Ti substrate using a one-step atmospheric plasma spraying process. Under 808 nm NIR irradiation, the water contact angle of the b-TiO₂ coating increased from 0° to 154.4 ± 4.0°, indicating a transition from a superhydrophilic to a stable superhydrophobic state. FTIR and XPS analyses showed that NIR-induced photothermal heating promoted the removal of surface hydroxyl groups and the passivation of oxygen-deficient sites, thereby driving the wettability transition. Among TiO₂ coatings with hydrophilic, intermediate-wettability, and hydrophobic surfaces, the hydrophobic coating effectively directed macrophage polarization toward the anti-inflammatory M2 phenotype and delivered slightly superior osteoblast activity. It also markedly inhibited Staphylococcus aureus adhesion, achieving an anti-adhesion efficiency of 98.61%. These findings demonstrate that NIR irradiation can regulate the wettability of plasma-sprayed b-TiO₂ coatings and provide concurrent immunomodulatory and antibacterial effects. This approach may support the development of light-responsive surfaces for orthopedic implants. Full article

Automated wildlife monitoring systems are essential for studying bat populations in natural environments, where nocturnal behavior, high flight speeds, and limited illumination make conventional observation difficult. This paper presents a modular multi-sensor vision system that integrates RGB, near-infrared (NIR), and depth cameras for automated bat monitoring. The proposed architecture consists of one main module and two secondary modules that can be configured into multiple operating modes according to monitoring requirements. The main module operates independently to perform real-time habitat reconstruction using an integrated depth camera or bat detection using a YOLO-based model. When combined with one secondary module, it forms a stereo vision system for three-dimensional localization; when combined with both secondary modules, it generates panoramic images that substantially expand the field of view for monitoring large cave entrances and other complex environments. The proposed modular architecture enables flexible deployment while supporting multiple sensing configurations within a single platform. The modular design provides scalability, geometric consistency through multi-sensor calibration, and flexible deployment, enabling accurate bat detection, habitat reconstruction, and wide-area monitoring within a unified sensing framework. The proposed system provides a versatile and scalable solution for adapting wildlife monitoring to different environmental conditions and observation scenarios. Experimental results demonstrate a detection precision of 0.893, a panoramic field of view of 119°, and real-time processing at 60 fps, validating the effectiveness of the proposed modular architecture. Full article

Wearable near-infrared spectroscopy (NIRS) enables non-invasive hemodynamic monitoring, yet conventional CW-NIRS systems suffer from limited temporal resolution, scalp-only measurement, and mandatory manual tuning to compensate for optical heterogeneity across subjects and sites. This work develops NIRSLINK, a modular cascaded wearable NIRS system for high-speed multi-site hemodynamic acquisition. Its flexible probes adopt spring-floating optics with a standardized 30 mm optode separation, integrating dual 735/850 nm LEDs, silicon photodiodes, and a two-stage closed-loop tuning algorithm. A single probe achieves a peak sampling rate of 3 kHz, and up to eight cascaded probes form 52 valid channels, with a signal-to-noise ratio (SNR) of 78.61 ± 7.03 dB and an optical dynamic range (DR) of 101.32 ± 12.41 dB. Phantom experiments verify its millisecond temporal resolution and high sensitivity to blood flow and hemoglobin variations. In vivo trials, including the Valsalva maneuver, forearm occlusion, and two-back cognitive tasks, demonstrate simultaneous recording of hemoglobin concentration shifts, pulse waveforms, and beat-to-beat pulse transit times (PTTs). NIRSLINK supports hemodynamic measurements across multiple anatomical locations, including the forehead, forearm, upper arm, and thigh, covering both cranial and peripheral body regions. The embedded auto-tuning module stabilizes signals from the forehead, forearm, and other regions within the optimal ADC range without manual adjustment, preventing signal saturation and SNR degradation. This scalable adaptive platform overcomes critical drawbacks of traditional wearable NIRS, applicable to cognitive neuroscience, non-invasive cardiovascular assessment, and ambulatory physiological monitoring, and provides design references for multi-site optical sensors. Full article

Photodynamic therapy (PDT) is a promising minimally invasive treatment for non-melanoma skin cancer (NMSC). Still, its clinical efficacy is limited by light attenuation and drug distribution within tumor tissue, which restricts photosensitizer activation in deeper tumor regions. PDT with 5-aminolevulinic acid (ALA) is widely used as a precursor to induce accumulation of protoporphyrin IX (PpIX), followed by red light irradiation; it is effective for superficial lesions but often fails to achieve complete tumor control in thicker tumors, contributing to long-term lesion recurrence. To address this limitation, we investigated a two-photosensitizer, two-wavelength-PDT approach that combines ALA with indocyanine green (ICG), a near-infrared (NIR)-responsive photosensitizer that can be activated at greater tissue depths. This also promotes different cellular death targets, since ALA-mediated PDT mainly induces direct tumor cell killing through apoptosis and necrosis, whereas ICG-mediated PDT may contribute to treatment effects. In this study, a preclinical model of cutaneous squamous cell carcinoma (SCC) was used, with animals assigned to control, single-photosensitizer PDT (ALA, ICG), and combined photosensitizer PDT treatment groups. The effect of photosensitizer administration and light irradiation sequence on therapeutic efficacy was also investigated, and tumor progression and survival outcomes were monitored over time. The results indicated that the combined ALA+ICG-PDT approach produced the most sustained suppression of tumor growth and significantly prolonged animal survival compared with single-photosensitizer PDT. Importantly, these findings demonstrated a strong dependence on the sequence in which the photosensitizer and light are applied. These findings indicate that integrating photosensitizers activated at complementary wavelengths may improve treatment coverage across the tumor and partially overcome depth-related limitations associated with PDT, representing a promising strategy to enhance PDT efficacy for NMSC and other solid tumors. Full article

Contemporary mural paintings contribute a significant part of urban cultural heritage, yet their conservation remains challenging due to the complex materials used and the aggressive conditions of the urban environment. Among the main deterioration factors, vandalic graffiti is particularly problematic, as its removal must be carried out without damaging the original paint layer, which often has a similar chemical composition. In this context, laser cleaning is a promising alternative to conventional mechanical and chemical methods. This study evaluates the effectiveness and selectivity of a nanosecond Nd:YAG laser (1064 nm) for the removal of a blue alkyd graffiti spray paint applied over mock-ups prepared with alkyd and polyvinyl acetate (PVA) paints on concrete substrates. The cleaning results were evaluated by stereomicroscopy, colour spectrophotometry, measurement of static contact angle, profilometry, near-infrared (NIR) hyperspectral imaging, Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) to assess physical and chemical changes after laser treatment. The results show that the effectiveness and selectivity of the process depend strongly on the chemical composition of both the vandalism layer and the original paint system, highlighting the importance of preliminary material characterisation prior to laser cleaning interventions. Although laser cleaning enabled the partial or substantial removal of the blue alkyd graffiti in all cases, alkyd-based paints exhibited greater resistance to laser irradiation and allowed more effective graffiti removal with fewer surface alterations than PVA-based paints. Among them, the green alkyd paint achieved the highest cleaning efficiency. These results indicate that the interaction between laser radiation and the materials was governed not only by the binder type, but also by the pigment composition and the optical properties of the paint layers. Full article

A near-infrared (NIR) light transmission imaging system was constructed using a stereomicroscope equipped with an 860 nm NIR LED light source to evaluate its applicability for observing wood anatomical structures. Species identification of wooden Buddhist statues is important for clarifying their provenance and production period, but such objects require non-destructive examination, and surface darkening from aging and soot deposits often limits observation under visible light. Fifteen wood species used for Buddhist statues and other cultural and architectural properties—eight hardwoods and seven softwoods—were examined, and NIR transmission images of the transverse section were obtained under air-dried and water-saturated conditions. Under air-dried conditions, NIR transmittance differed among species and between heartwood and sapwood, revealing anatomical structures such as vessels, growth-ring boundaries, and resin canals. Under water-saturated conditions, transmittance increased in all species and resin-canal structures became more distinct in softwoods, although some image blurring occurred. These results indicate that NIR transmission observation is effective for non-destructive species identification of wooden cultural properties, and that wood moisture strongly affects the resulting transmitted image. Full article

Functional near-infrared spectroscopy (fNIRS) has become an important signal source in motor imagery (MI) brain–computer interface research due to its non-invasive nature and high application flexibility. However, fNIRS signals exhibit significant inter-subject variability, complementary information from oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR), and complex spatiotemporal dynamics, making their efficient and robust classification challenging. To address these issues, this paper proposes a Dual-Stream Gated Fusion Network (DSGF-Net). This model employs a dual-branch architecture to perform complementary feature modeling of fNIRS signals: one branch focuses on extracting multi-scale temporal dynamic features, while the other learns the spatial distribution of hemodynamic features across channels, thereby effectively characterizing the signals from different perspectives. Upon this foundation, a gated fusion mechanism was designed to adaptively adjust the importance of different feature dimensions after the fusion of the two feature streams, thereby enhancing the discriminative power of the fused representation. On two public datasets, MI and UFFT, experimental results based on leave-one-subject-out (LOSO) cross-validation show that the proposed method achieves competitive performance across metrics such as classification accuracy, F1-score, and Kappa coefficient. Furthermore, a comparative analysis of performance under different network component configurations validates the contributions of the dual-branch structure and the gated fusion mechanism to performance improvements. Furthermore, complexity analysis results show that DSGF-Net achieves superior classification performance while maintaining a relatively small parameter size, striking a good balance between performance and computational complexity. DSGF-Net provides an effective, lightweight deep learning framework for offline fNIRS-based motor task classification, with potential applications in cross-subject BCI systems and brain signal decoding. Full article

Routine monitoring of cadmium (Cd) contamination in rice is essential for public health protection and agricultural trade security. Conventional chemical detection methods are environmentally unfriendly, labor-intensive, and slow. This study presents a rapid, accurate classification approach based on near-infrared reflectance spectroscopy (NIRS) for discriminating Cd-contaminated rice from uncontaminated rice. Five spectral preprocessing methods and three variable selection algorithms were systematically evaluated for their influence on model performance. Classification models were developed using partial least squares discriminant analysis (PLS-DA), K-nearest neighbors (KNN), and support vector machines (SVM). Second derivative (2D) preprocessing yielded the greatest performance gains, raising KNN and SVM test-set accuracy from 73% and 88% to 93% and 91%, respectively. Among the variable selection strategies, the successive projections algorithm (SPA) proved most effective. Under optimized conditions, PLS-DA achieved the best overall performance, attaining 92% accuracy, 89% specificity, and 95% sensitivity on the test set. These results demonstrate the strong potential of NIRS coupled with machine learning for rapid, large-scale Cd surveillance in rice, providing robust technical support for grain quality monitoring and low-cadmium variety breeding programs. Full article

Afghanistan’s Panjshir Valley is an important emerald-producing region in Asia. In this study, emeralds from Kamar Safid in Southeastern Panjshir were investigated by Fourier-transform infrared (FTIR), Raman spectroscopy, ultraviolet–visible–near-infrared (UV-Vis-NIR) spectroscopy, and laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS). These Kamar Safid emeralds are generally small, light-green-to-green crystals. Microscopic observations revealed abundant acicular and tubular three- or two-phase fluid inclusions, with transparent feldspar-group mineral inclusions. Solid phases in the fluid inclusions commonly consist of carbonate crystals or several transparent halite daughter crystals. FTIR spectra of samples indicated that the absorption of type II H₂O was higher than type I H₂O in the emeralds from Kamar Safid. The UV-Vis-NIR spectra are characterized by Cr- and V-related absorption bands, which are stronger than Fe-related absorptions. LA-ICP-MS results indicate slightly higher V contents and lower Cr contents than emeralds from other Panjshir mining areas. The relatively low total Cr and V contents of Kamar Safid emeralds account for the overall lighter color, suggesting that Cr and V are the principal chromophores, whereas Fe secondarily modifies hue. Rb, Cs, and Sc contents are 6.2–24.3 ppm, 11.9–141.6 ppm, and 92–1461 ppm, with total alkali contents of 4903.10–14,257.18 ppm. Cs-Rb, Cs-Sc, Li-Cs, and Li-Sc binary logarithmic diagrams indicate enrichment in Sc and Rb and depletion in Li and Cs. Full article

Microplastic pollution has become an emerging concern in feed safety and animal-derived food safety. This study applied near-infrared spectroscopy (NIR) combined with machine learning to perform qualitative and quantitative analysis of seven common microplastics in chicken feed, including polyamide (PA), polycarbonate (PC), polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC). A total of 840 samples with microplastic mass fractions ranging from 0.01% to 1.00% were prepared. Near-infrared spectra were acquired in the 1100–2200 nm range, and multiple spectral preprocessing methods and models were evaluated. The Synthetic Minority Over-sampling Technique (SMOTE) was introduced to assess the effect of data augmentation. For classification, the Extremely Randomized Trees (ET) model achieved the best performance, with an accuracy and F1-score of 0.9603 and 0.9602, respectively. For regression, performance varied among polymers, with PVC showing the best quantitative prediction performance using MA preprocessing combined with SVR (test set R² = 0.9851), while the raw-spectrum SVR model achieved R² = 0.9846 and RPD = 8.1642. Spectral preprocessing and data augmentation produced polymer-dependent changes in model performance. These results support portable NIR spectroscopy as a rapid, non-destructive screening approach under the controlled single-polymer conditions studied; mixed-polymer and field robustness require further validation. Full article